Abstract
Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of the most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machine learning-based finite-field approach that combines two neural network models: one predicts atomic forces under applied electric fields, while the other describes the corresponding charge response. Both models are trained entirely on first-principles data without employing any classical approximations. As a proof-of-concept demonstration in a prototypical Au(100)/NaCl-(aq) system, this approach accelerates fully first-principles finite-field simulations by roughly 4 orders of magnitude compared to ab initio molecular dynamics, allowing the extrapolation to cell potentials beyond the training range and accurate prediction of Helmholtz capacitance. Interestingly, we reveal a turnover of both density and orientation distributions of interfacial water molecules at the anode, arising from competing interactions between the positively charged anode and adsorbed Cl(-) ions with water molecules as the applied potential increases. This novel computational scheme shows great promise in efficient first-principles modeling of large-scale electrochemical interfaces under potential control.